The Causal Effect of Scholarships ... - amsacta.unibo.itamsacta.unibo.it/4083/1/WP968.pdf · ∗Contact author: [email protected] 1. 1 Introduction Given the extensive social

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The Causal Effect of Scholarships Targeted at Low Income Students on

Performance: Evidence from Italy

Veronica Rattini

Quaderni - Working Paper DSE N°968

The Causal Effect of Scholarships Targeted at Low Income

Students on Performance: Evidence from Italy

Veronica Rattini∗

Abstract

This paper exploits discontinuities in the assigment of scholarships targeted at low income

students in an Italian University in order to evaluate the effects of monetary incentives on students’

academic achievement. Results reveal positive and sizeable causal effects both in terms of credits

and grades. Gender differentials also emerge: male students drive the results on credits outcome

while females students drive the effect on grades. These results suggest that the scholarship design

contributes to reducing the probability of delayed graduation (“Fuori Corso” problem).

JEL codes: H5, I21, I28, .

Keywords: Human Capital, Monetary Incentives, Regression Discontinuity

∗Contact author: [email protected]

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1 Introduction

Given the extensive social and private benefits that result from tertiary education, inclusive ac-

cess and a well functioning system are essential for achieving social justice and enhancing national

human capital. Furthermore, if one considers the strong correlation between tertiary education en-

rollment and family background (McPherson and Schapiro, 2006), concrete and effective initiatives

are necessary to provide better opportunities of access and success for students from lower income

families and minority groups.

In Italy, the “Right to Study” Constitutional principle responds to such equity purposes by

providing different types of services for students from lower income family who want to enroll at an

university program. In this research, I focus on the “Right to Study Scholarship”. In particular,

I analyze how the monetary provision of such scholarship causally impact on students’ academic

achievements by applying a Regression Discontinuity analysis.

I interested in understanding whether these monetary incentives could serve to boost students’

performance by reducing direct and indirect college costs. Moreover, since the maintenance and

non-reimbursement of the mean-tested scholarship are conditional on the achievement of minimum

credit requirements, the analysis gives the opportunity to understand whether such requirements

are adequately defined in order to serve the purpose of enhancing students’ performance and the ex-

tent to which they could alleviate the problem of being “Fuori Corso” (delayed graduation), which

is a wide-spread phenomenon in many countries (see, among others, Hakkinen and Usitalo, 2002;

Van Ours and Ridder, 2002; Brunello and Winter-Ebmer, 2003; Bowen, Chingos and McPherson,

2009).

In the economic literature, a growing number of empirical studies addresses the question of

how monetary incentives shape students’ educational choices. In particular, a number of papers

investigate the effect of such incentives on the choice of school program and on degree completion

(Hansen 1983; Kane 1994; Dynarski 2000,2003; Van der Klaauw 2002; Goodman 2008). Other

works, mostly from US, have focused on the effects on academic performance and do not find uni-

vocal results (Angrist and Levy 2002; Leuven et al. 2003; Cornwell at al. 2003; Dynarski 2005;

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Belot et al. 2007; Angrist et al. 2009).

For what concerns the case of Italy, the evidence is not uniform and the present study ought

to provide new insights on the issue. In particular, Garibaldi et al. (2012) find that an increase in

college cost, in response to delayed graduation, exerts a remarkable effect on the on-time comple-

tion rate, using evidence from Bocconi University. In line with this evidence, I find that monetary

incentives speed up the number of credits a student achieves in the first year of the degree without

prejudice to, but actually increasing, average grades. This effect can arguably help the “Fuori

Corso” problem. In other words, the need-based scholarship leads low-income students to exert

more efforts in order to meet minimum credit requirements and to avoid losing their grants (which

is equivalent to an increase in university costs).

De Paola et. al (2010) find that “financial rewards increase student performance both in terms

of number of credits acquired and grades obtained at exams”. My results are in line with such

evidence, even if their institutional context is different with respect the one analyzed here. In

fact, they analyzed the effect of an extra reward financed through the European Social Found for

above-average students, which could not give the same incentives as the Right to Study Scholarship.

Mealli and Rampichini (2012) have, instead, analyzed data regarding four Italian universities

and they show that the “Right to Study Scholarship” (which is the same Italian institution that

we here analyze) prevents students from droping-out. However, they find that the effect vanishes

for lower income students. Here we provide new evidence from the “Right to Study Scholarship”

at the University of Bologna showing that such effects on performance for lower income students

are not only significant but are also remarkably large.

My results are also at odds with Schizzerotto et al. (2012) who studies the effect of a merit-

based scholarship at the University of Trento and do not find any significant effect on academic

achievement. Such contrast might have to do with the fact that they use a relatively small sample

from survey data, which are more sensible to measurement errors. In my case, the administrative

dataset I use allows to gain precision on the estimates.

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The paper is organized as follow. The next section outlines how the present study relates to the

literature developed so far. Section 3 analyzes the institutional framework and Section 4 describes

the data and methodology. In section 5, I will present and discuss results on the causal effects of

scholarships on different measures of academic achievement. Section 6 discusses the robustness of

the findings and Section 7 concludes.

2 Literature Review

In recent years a growing number of interventions designed to increase tertiary education enrol-

ment, completion and effectiveness took place. A number of these programs targeted low income

students. While most of the interventions are based on monetary incentives, program design fairly

varies. Examples of the designs are: merit and need-based scholarships, tuition subsidies or part-

time working programs.

Such policies are justified by the fact that large economic returns related to tertiary educa-

tion have been clearly documented and they do not constitute only private benefits (Barro 1991,

Acemoglu, D., and J. Angrist 1999, Temple 2000, Dasgputa 2001, Glaeser et al. 2004). Positive

externalities resulting from education provide a strong economic reasoning for such interventions.

Moreover, it has been shown that the effort provided by students in schooling activities is one of

the most important factors in the human capital accumulation, possibly more effective than teacher

quality and school resources (Costrell, 1994; Bonesronning, 2004).

Further justifications come from the fact that, in economic theory, monetary aids are supposed

to act as incentives promoting effort and scholastic performance (Lazear 2000) being, therefore,

helpful in reducing the still nowadays large percentage of young students who face severe problems

in educational choice. It is well documented, in fact, that the share of students who drops-out

the tertiary education is commonly high even in developed countries and that time to degree com-

pletion has increased remarkably in the last two decades. 1 Therefore, the interest in how such

1For example, Bound et al. (2006) for US and Brunello and Winter-Ebmer (2003) for Europe

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aid programs are related to students’ performance started to grow in economic research. However,

there seems to be much room from further research since results are by no means univocal: some

studies provide evidence of a positive impact, while others find rather weak effects. This hints at

the necessity of better understanding of how different policy designs implicate in different effects

on performance and how these effects vary across different groups of subjects.

In non-experimental research the effectiveness of academic support services is mixed and do

not provide a realiable causal measure of such relationship (e.g., surveys by Bailey et al. 2005;

Pascarella et al. 1991; Lotkowski et al. 2004, and Wyckoff et al. 1998). On the other hand, better

designed studies using experimental and quasi-experimental designs draw a more clear picture.

With reference to the US case, Bettinger (2004) shows that a mean-tested programme of financial

assistance exerts a remarkable reduction in the drop-out rate in Ohio University. Dynarski (2005),

who analyzed the effect of the Georgia’s Helping Outstanding Pupils Educationally (HOPE) merit-

based program, finds a positive effect in college completion by 3 to 4 percentage points. Cornwell at

al. (2003) analyze the same program and find that the shift from need- to merit-based aid increases

the probability to withdraw and reduces the number of average credits obtained. Angrist and Lavy

(2009) evaluate the effectiveness of financial rewards on the academic achievement of Israeli stu-

dents. They find that the program has led to relevant effects for female but not for male. Differences

among genders emerge in Angrist, Lang and Oreopoulos (2009), who conduce an experimental eval-

uation on students enrolled to a large Canadian University. The results, in fact, show that while

male students were not affected by the program, female ones improved their academic performance.

For what concerns Europe, Leuven et al. (2003) analyze the effect of merit-based rewards

for students enrolled at the University of Amsterdam. They find that only high-ability students

and those with highly educated fathers obtain better academic achievement when assigned to the

high reward group. For the whole sample taken together instead, there was no effect. Belot et

al. (2007) exploit a Dutch reform in the educational system in order to understand the effect of

student assistance on academic performance and students’ time allocation. They find that there is

a small positive effect on the grades but drop-out and time allocation of students remain basically

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unchanged. De Paola et al. (2010) analyze the effect of a randomly assigned merit-reward for

students enrolled at University of Calabria. They find positive effects of the monetary incentives

on average grades. Garibaldi et al. (2012) find that at Bocconi University in Milan, the increase

in the tuition payments for “Fuori Corso” students, i.e their enrollment in the university system

extended beyond the legal length of their program, exert a remarkable effect on on-time completion

rate.

Mealli and Rampichini (2012) analyze data regarding four Italian universities and show how public

provided university scholarships, under the “Right to Study Scholarship”, prevent drop-out for those

students from medium income families, while for poorer students the effects are not significant.

Schizzerotto et al. (2012) looks instead at the incentives assigned to students from low income

families introduced in a well-defined area in the North-East of Italy, the administrative province of

Trento. They find no effect whatsoever on the drop-out rate, the average mark and the number of

credits achieved.

In sum, from a broad review of the recent empirical literature, it becomes clear that the overall

evidence on the effect of monetary incentives is rather controversial, ranging from studies finding

no effects whatsoever to studies finding large and positive effects on performance.

The goal of this paper is to enrich this debate showing the results of a mean-tested program

carried out in the Italian context. In particular, I try to disentangle the pure effect of the so called

“Right to Study Scholarship” from other confounding factors on students’ academic achievement

through applying a Regression Discontinuity Design. The idea underlying the “Right to Study-

Scholarship” is to reduce the cost related to tertiary education attendance in order to prevent low

family income students from being excluded, drop-out, or under-perform with respect to other

pupils due to financial constraints. This study show how such institution could be used to serve

the additional pourposes of reducing the percentage of “Fuori Corso” students and, more generally,

in fostering the accumulation of human capital.

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3 The Institutional Framework

Tertiary education in Italy is generally accessible to students with an high school diploma, indepen-

dently of the type obtained (scientific, classical, professional). Moreover, the Italian Constitution

states in art. 34 that University education should be accessible also to those students who do not

have the financial resources and the that the Italian Republic furthers the realization of the, so

called, “Right to Study” by providing scholarship and other allowances to students.

“Pupils of ability and merit, even if lacking financial resources, have the right to attain

the highest grades of studies. The republic furthers the realization of this right by schol-

arships, allowances to families, and other provisions, to be assigned through competitive

examinations.”

To enhance equal opportunity and fair access, the region offers many types of services: al-

lowances for international mobility, housing and meals services, services for people with disability,

vouchers for education programs (Master, High-level education, etc.), fiduciary loans, and part-

time working possibilities. The present study will be focused on the “Right to Study Scholarships”

offered for the University of Bologna. The “Right to study Scholarship”, from now on “RSS”, is

one of the services provided by the Region of Emilia-Romagna, where the University of Bologna

is located, to promote access and equity in education. In particular this research want to measure

what are the effects of such monetary incentives on students’ academic achievement.

The “Right to Study” service in Emilia-Romagna has generally a large coverage, both in terms

of the number of recipients and of the amount of financial resources provided. Specifically, 13.475

students received a RSS over a total of 98.357 students in the region (13,7%) for the 2008/2009

academic year. 2 Furthermore, to finance the RSS for the same academic year, the Region of Emilia-

Romagna collected: from national resources e151.986.000, from regional resources e158.120.201 ,

from regional taxes e171.085.441, making a total of e481.191.642. 3

In this perspective, it becomes quite important to know how these grants shape students’ in-

centives and, in particular, whether they have any effects on students’ academic achievements,

2Data from “Ministry of Education, University and Research - MIUR”3Data from “Ministry of Education, University and Research - MIUR”

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especially in Italy where students’ profiles and performance are quite peculiar. In fact, it has

been estimated 4 that 36% of students are “Fuori Corso”, i.e those whose their enrollment in the

university system extended beyond the legal length of their program, and 21,7% of students drop-

out. Moreover, the OECD 5 estimates that in Italy 23,2% of young in the 15-29 age bracket are

NEET-Not in Education, Employment, or Training and that the employment rates of the youths

in 25-29 age bracket is one of the smallest in Europe. In this context, therefore, proper monetary

incentive schemes could play a role not only in providing fair access to the University but also in

fostering students’ performances and enriching national human capital, as through the prevention

of university drop-out and the reduction of the share of “Fuori Corso” students.

The regional agency appointed for the distribution of the “Right to Study” services is ER.GO

and from 2008 onward, the agency have fully covered all the scholarship’ applicants, a 100% suc-

cessful rate. The application to scholarships is made before the academic year starts. 6 Results on

allowance eligibility are published within few months and the first installment of the grant (50% of

the yearly allowance) is paid due the end of calendar year. The second half of the financial transfer

is bind to the satisfaction of precise credit requirements, which are known ex-ante.

The scholarships differ according to the student status which depends on their place of residence:

“In sede”, i.e their university’s course is in the city of residence or they do not live more than 45

minutes far (by public transport) from the university’s center; “Fuori sede”, i.e their university’s

course is more than 90 minutes far (by public transport) away from the city of residence, “Pen-

dolari”, i.e their university’s course is between 45 and 90 minutes away from the city of residence.

There is no distinction, on the other hand, with respect to the level of their degree. Within groups,

“In Sede”, “Fuori Sede”, “Pendolari”, scholarships are assigned according to three thresholds on

one income indicator of the family, ISEE. Furthermore, eligibility is always conditional on a max-

imum value of a further wealth indicator of the family, ISPE, which shall not exceed e32.320,64.

The ISEE indicator is given by the annual after-tax income plus the 20% of family assets, adjusted

for the family size by means of an equivalence scale; the ISPE is instead an indicator based just

4AlmaLaurea – Annual Report on University’ Graduates 20135Education at la glance 20136At the University of Bologna all the courses start from September/ October to June/July of each year.

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on the family assets and it is adjusted for the family size by means of an equivalence scale. The

amount of scholarships are the following:

Table 1: Scholarship Value

ISEE Threshold “Fuori Sede” “Pendolari” “In Sede”

Until e 11,927.35 e 5,073.78 e 3,043.88 e 2,255.11

From e11,927.36 to e 14,909.19 e 3,942.83 e 2,420.89 e 1,828.83

From e 14,909.20 to e 17,891.03 e 2,811.88 e 1,796.93 e 1,402.53

For the undergraduate freshman, scholarship’s assignment is conducted just on the basis of the

two economic indicators, ISEE and ISPE. The master freshman, should have obtain, in addition,

at least 150 credits in the undergraduate program. On the top of that, the grant maintenance

and right not to reimburse the scholarship are conditioned to precise credit requirements. Namely,

undergraduate students should obtain 25 credits (out of 60) by the end of the first academic year

and master students should obtain 30 credits (out of 60) by the end of the first academic year,

independently of their status, “Fuori Sede”, “In sede”, “Pendolari”.

For second year applicants, the assignment is conducted on the basis of the two economic indi-

cators, ISEE and ISPE, and on the basis of the credits obtained in the first year of study. Precisely,

in order to be eligible for the scholarships, students should have obtained at least 25 credits in

the first year of undergraduate course or 30 credits if enrolled in the first year of a master course.

Moreover, in order not to reimburse the grant, recipients should obtain at least 80 credits at the

end of the second year of enrolment (both for undergraduate and master program). The same

credit requirement (80 credits by the end of the second year) is used for the third year application,

together with the two economic indicators. In addition, students enrolled to the third year of the

undergraduate program should obtain at the end of the academic year 135 credits not to refund

ER.GO for the scholarship amount.

On the top of credit requirements, there are also “credits bonus”. The design of the RSS offers

the possibility to students to use credits bonus, ie. they can add a limited numbers of credit bonus

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Table 2: Credits Requirements by the end of each year

Type of course 1st Year 2st Year 3rd Year

Undergraduate 25 80 135

Master 30 80 -

to the number of actual completed credits, in order to meet the requirements. Specifically, students

have the right to use:

a) 5 credits bonus if they asked to satisfy the 2nd year requirement;

b) 12 credits bonus if they asked to satisfy the 3rd year requirement;

Recipients can take advantage of using current year remaining credit bonus in further years,

however these are not cumulative.

4 Data and Methodology

The data are provided by the regional agency which is in charge of delivering the RSS, ER.GO,

and by the University of Bologna. The advantage to use administrative data is that observations

on performance and income status are more precise than survey data, therefore it allows for more

precise estimates and decreases the risk of measurement errors biases.

The academic year of reference is the 2008/2009 and the sample includes all the freshman admit-

ted in all the twenty three faculties of the University of Bologna. 7 The data set includes students

enrolled at different levels of Tertiary education: Undergraduate level and Master level. The data

set also includes information on students’ demographic characterstics and on university-related

variables: high-school grade, credits and average mark obtained, university center (Bologna, Forlı,

Cesena, Rimini, Ravenna), faculty, graduation course, type of scholarship obtained (“In Sede”,

“Fuori Sede”, Pendolari”), ISEE and ISPE.

7From academic year 2012/2013 the University of Bologna had change the organisation from 23 faculties to 11 Schoolsand 33 Departments.

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Table 3 in the appendix provides descriptive statistics for the sample of students. About 60%

percent of the students are female. The average age is 21 years old. The geographical distribution is

quite symmetric as 37% of students are coming from the North, 26% from the Center, 28% from the

South of Italy and 9% are foreign students. The High School grade in Italy ranges from 60 to 100;

the sample mean equals 81.6 points. The high school grade is set to missing for foreign students

since I do not have enough information to perfectly convert the foreign scale into the Italian ones.

The average number of credits obtained during the first year of enrollment is around 31 with an

average grade of 26. Some data on average grade were missing in the data provided by ER.GO and

could not be recovered until the time being. “In Sede” and “Fuori Sede” scholarships’ students are

quite evenly distributed in the sample.

In order to study the effect of scholarships on students’ performances, I use a Regression Dis-

continuity Design. The RDD has been largely used in economics and behavioral sciences and was

firstly introduced by Thistlethwaite and Campbell (1960). The attractiveness of such design is that

it allows to identify and estimate treatment effects in a context similar to a formal randomized ex-

periment. In particular, data on random assignment of treatment and control groups are commonly

used in medical analysis. In economic research and, precisely, in the evaluation of social programs,

the use of random assignment is mostly controversial and remains unusual. Under this perspective,

RDD succeeds in establishing soft conditions that allow to identify and estimate treatment effect

on observational studies, without relying on parametric evaluation model, often criticized in the

literature (for example Lalonde(1986)).

The identification and estimation issues for the RDD were formalized in the work of Hahn,

Todd, and van der Klaauw (2001), which describes the minimum set of conditions under which it

possible to nonparametrically identify the treatment effect.

It is typically imagined that, for each individual i, there exists two potential outcomes: Zi(1)

if the unit is exposed to the treatment and Zi(0) if not exposed. The difference Zi(1) − Zi(0)

represent the causal effect of the treatment. The real problem of causal inference is that we cannot

observe the pair Zi(0) and Zi(1) simultaneously (Holland (1986)). We therefore typically focus on

average effects of the treatment, E[Zi(1)|Y ] and E[Zi(0)|Y ] over (sub-)populations, rather than at

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individual level. The idea of the quasi-experimental RD design is to exploit the discontinuities in

the treatment assignment. Precisely, assuming that units close enough to the threshold are similar

except for their treatment status, the observed difference in outcomes can be attributed just to the

treatment. Formally, consider that all individuals to one side of the a certain threshold, Y = c,

are exposed to treatment and all those to other side are denied treatment. Therefore, we only

observe E[Zi(1)|Y ] that is the average treatment effect for the treated units and E[Zi(0)|Y ] for the

untreated ones. By the continuity assumption is possible to show that we can estimate the average

treatment effect as follow:

ATE = limε→0

E[Zi|Yi = c+ ε]− E[Zi|Yi = c− ε] = E[Zi(1)− Zi(0)|Yi = c] (1)

In our setting, the set of outcome variables, Zi, consists of: number of credits and average mark

obtained in the first year on enrolment, a constructed index of performance 8 and by a binary vari-

able taking values [0,1] if the student have satisfied the credits’ requirements. The treatment is a

dummy variable taking value 1 if the individual receives the higher scholarship (around e840 more

with respect to the counterfactual) and 0 otherwise; the treatment rule is determined by ISEE’s

thresholds. For the first threshold, the rule is the following: students with the income indicator,

ISEE, lower or equal to e11.927,35 are assigned to the higher scholarship, while students with

ISEE between e11.927,36 and e14.909,19 are assigned to the lower one; the difference between

the two scholarships is of about e950. For the second threshold, students with an income between

e11.927,36 and e14.909,19 receive a scholarship of e730 higher than the students with ISEE be-

tween e14.909,20 and e17.891,03. The mean aggregate difference of e840 is computed giving equal

weight to the change at the two thresholds.

In order to aggregate students with ISEE near the two thresholds, ci = (c1, c2), I constructed a

8The index is constructed by firstly normalizing the credits and the average obtained between [0;1] and then bycomputing the weighted average of the two measures, giving two equal weights.

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new variable Yi that is define by:

Yi =

0 if ISEE = ci

+ (ISEE − ci) if ci ≤ ISEE ≤ ci +ci+1 − ci

2

− (ISEE − ci) if ci −ci+1 − ci

2≤ ISEE ≤ ci

(2)

Notice that the positive (negative) values of Yi are computed taking just the values of ISEE

that are greater (smaller) than the threshold values plus the first half of the consecutive threshold’

bracket. In this way we are not confounding the scholarships’ incentives of students near the first

threshold with those of students near the second threshold, and the ones of students near the second

threshold with those near the third threshold.

From the treatment analysis on the two thresholds, I will estimate the Local Average Treatment

Effect (LATE). In particular:

Zi = α+ F (Yi) + γDi + εi (3)

where the Zi s a vector of outcomes variable, F (Yi) is a polynomial of 4th degree on the constructed

variable defined as Yi, Di is the dummy variable taking value equal to 1 if students are assigned to

the higher scholarship. Then it is possible to demonstrate that:

γ = E[Z|Y = 0+]− E[Z|Y = 0−]

is an unbiased estimator of the LATE.

5 Results

The results are drawn combining data of students who have a value of ISEE near the first and the

second scholarships’ thresholds (e11.927,35 and e14.909,19 respectively). By aggregating those

data, the analysis gains in term of precision but we lose information on how the effects of interest

vary with the ISEE and with the students’ status (“In Sede”, Fuori Sede”, “Pendolari”). The

results are presented in Table 4.

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The local average treatment effects are estimated allowing for different polynomials on the two

sides of the aggregated threshold. To check for the robustness of the results, I use four specifications

with polynomials of different degrees, from the 1st to the 4th degree. As standars in such method-

ology, when I increase the order of the polynomial form, I simultaneously enlarge the observational

range in order to avoid losing the precision of estimates. More precisely, for the 1st polynomial

degree I used a bandwidth of ± e500 around the aggregate ISEE threshold. For the 2nd degree,

I add e350 obtaining a range of ± e850 while for the 3rd degree we use those observation lying

around ± e1200 with respect the aggregate threshold. For the 4th degree we use a bandwidth of

± e1400, the maximum possible range (half of the distance between the first and second threshold).

As it is shown in table 4, students who receive an higher scholarship amount, on average e840

more, have better results on academic achievement during the first year of university enrollment.

In particular, according to the specification used, students with the higher scholarship acquire from

9 to 18 credits more with respect to students with a lower scholarship during the frist year. Con-

sidering that the regular number of credits for the first year is 60 and that the average number of

credits obtained in the sample is around 32, the size of the effect is arguably large (from 28% to 56%).

The average of first year grades of those students with higher scholarships is around 2 points

higher than the one of those with a lower financing. The grades scale in Italy ranges from 18

(sufficiency grade) to 30 points (plus laude). Since the sample mean of the average first year grade

is of about 26 points, the estimated causal effect of an e840 higher scholarship is of about the 7%

on the average mark.

It is important to notice that, fellowship need not be reimboursed if the credit requirements is

achieved. Students aiming at fullfilling this requirement only may pass more exams but with lower

grades. I check if this is the case by looking at the effect on students’ GPA at the end of the year.

Pure credit requirements seem to be sufficient to increase students’ performance not only in terms

of credit achievement but also with respect to marks. In fact, as shown in the Panel C, an index of

performance (made out of an average on normalized credit achievement and average marks) jumps

of about 0.1 decimal points for students with the higher scholarship.

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Finally, I analyze the effect on the probability of having satisfy the credits requirements for

the maintenance of the scholarship, which are set equal to 25 (30) credits for the undergraduate

(master) students. The average rate of success in the sample is of about 78%, a fairly large fraction.

Nevertheless, the results show that the monetary incentives still have a positive effect on the prob-

ability of having satisfied this requirement. In fact, the effect goes from a minimum of 0.22 points

to a maximum of 0.47 probability points, which is equal to an average percentage effect of 28% and

60% respectively. What is clear from the analysis is that the current scholarship design could be

further used as an effective instrument for reducing the percentage of “Fuori Corso” students.

It is interest to contrast this overall finding, that the RSS boosts students’ achievement, with

the results found in Angrist et al. (2009), which instead find no effects on first year grades, of

pure monetary incentives in a randomized experiment. A possible explanation of such divergence

is that in the Canadian contest, the STAR program consists in subsides on tuition, which do not

have the same role of the Right to Study Scholarship. In this sense, the RSS is designed for helping

those students who otherwise would not have the means to attend tertiary education and this is a

decisive incentive. The students analyzed in Angrist et al (2009) are already enrolled at the time

of the experiment in the University.

Moreover, the effect presented above adds a new insight for the Italian empirical literature. Dif-

ferently from Schizzerotto et al. (2012), who have not found any significant effect of mean-tested

fellowships on students’ performances, we show that monetary incentives are useful to enhance aca-

demic achievement. One reason for this discrepancy might be that since their work use survey data

and a relatively small sample to apply a Regression Discontinuity Design, their estimates might not

have been precise enough to allow them to find significant results. In addition, Mealli and Rampi-

chini (2012) have found significant effects of the RSS only on drop-out of students enrolled at the

Universities of Catania, Milan, Padova and Salerno, without focusing on students achievements.

They do not find any significant effect for the lower income students. This is in contrast with the

present results which are focused on lower income students.

I proceed with the analysis in Table 5 by looking at the scholarships’ effects on the probability

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of obtaining the total number of credits expected for the first year of enrolment at the date of 10

August 2009, which we take as an indicator directly related to the probability to be “Fuori Corso”.

9 Although these results have a positive sign (around 0.1 points) and seem to be fairly stable

over the different polynomial specifications, they are not significant. Given that the analysis above

suggets that the minimum credit requirements may have significant effects on performance (credits,

grades and probability of meeting the requirement itself), a possible experimentation for policy

would be making such minimum requirements closer to the regular number of credits expected

from students along the years (60 credits per year). This could allow the RSS to more effectively

serve the purpose of reducing the share of “Fuori Corso” students.

In order to gain more insight on the underlying relationship between the monetary incentives

and students performance, I analyze treatments effects on the two thresholds separately. The aver-

age increase on the scholarship amount at the first threshold is of around e950 while the difference

at the second threshold is mildly smaller, around e730. The estimates are reported in Tables 6 and

7.

For the first threshold, I find positive and significant effects for all the outcome variables. For

the second threshold instead, results are less clear cut. Treatment effects are only significant on

the number of credits and on the probability of having satisfied the maintenance requirements.

Considering that the sample size and estimated standard errors are similar around both thresholds,

such results suggest that the scholarship is more effective in boosting academic performance of

lower income students. Even if we consider that the average increase in grants around the second

threshold is about 24% lower, the decrease in the size of the effect on the number of credits achieved

and average grade is fairly lower than that. This interpretation suggests that policy makers could

allocate a higher share of funds to the less well-off recipients if the goal is to increase the number

of credits and grades.

Finally, I investigate how treatment effects change within sub-groups of population. More specif-

ically, I firstly analyze the results by students status, “Fuori Sede”, “In sede” and “Pendolari, and,

9“Fuori Corso” status is assigned when the student do not acquire 60 credits at the 31/03/2010.

16

unfortunately, I do not find any significant and consistent result for our different specifications. The

same happens when differentiating for Undergraduate and Master students. This may suggests that

the sample is not large enough to allow us to obtain precise enough estimates on these subgroups.

However, I was able to successfully exploit differences in effects between female and male stu-

dents. Results are reported in the Table 8 and 9, for female and male respectively. I find that the

large and significant scholarship effects on credit achievement are mostly driven by male students.

Although still positive, these effects for female are smaller and not significant. On the other hand,

the previous aggregate results on average grades are mainly driven by female students and they

are still positive but not significant for male. These results show that monetary incentives are

differently perceived between male and female students, as it is found in previous literature. In

the present research, I find pure monetary incentives to be effective in raising the academic per-

formance of both genders, but in a heterogeneous way. For male, the significant effect is on credit

achievement, while for female it is on grades.

6 Robustness

In order to further assess the validity of our results, I perform a standard graphical analysis both

on treatment and pre-treatment variables. Results are reported in Figures 1 and 2. I allow for

a 4th order polynomial on both side of the threshold and show a 90% confidence interval around

estimates. As it is possible to see, the graphical analysis confirms the results found with regressions

estimates. For all the treatment variables (except for the average for which results is less clear cut),

we find a significant and positive jump before the aggregate threshold, meaning that those students

just before the threshold, i.e with an higher scholarship, perform better than those just after it, i.e

with a lower scholarship.

Since it is impossible to test for the continuity assumption directly, I can test some implications

of it. More specifically, all the pre-treatment characteristics should have identical distribution, at

the limit, on the two sides of the cutoff. Therefore, in order to test for the validity of our identifica-

tion strategy, I test for a discontinuity in all of our pre-treatment variables applying the same four

17

specifications used for the post-treatment outcomes. I find no significant discontinuity on these in

any of these covariates, which is reassuring for the validity of our identification strategy. These

results are reported in Table 10.

In Figure 2 I show instead a graphical analysis on these pre-treatment covariates: Sex, Age,

High-School Grade, ISPE, Probability to be “Fuori Sede” students. Once more, I find no significant

jump in any of the pretreatment variables. Average High-School Grade seems to be higher for those

students with the higher scholarship, but, as shown in the regression estimates, this difference is

not significant. Moreover, I test further for this possible ability bias by looking at the probability

to be “Fuori Sede”. In fact, if students with higher High-School Grade, decide to go to college only

if they get higher scholarships, I should see a jump in the probability to be “Fuori Sede” since these

are those getting higher grants. Estimates show that this is not the case.

A further important issue to the validity of the identification strategy has to do with manip-

ulation of the assigned variable. Since the value of scholarships are based on the tax report of

family income (ISEE), there could exist some problems of manipulation. Fortunately, I can test

for this hypothesis. First, I test for the continuity of the ISPE (wealth indicator) variable at the

threshold, as for other covariates. Second, I perform the McCrary test (2008) on the density of

assignment variables around the thresholds. Results are available in Figure 2 and 3. As shown

in the Figure 3, there is no significant discontinuity in the density of the observations around the

threshold, supporting therefore the hypothesis of no manipulation on the assignment variable, ISEE.

Concluding, after performing all these robustness tests, we should be confident on the validity

of the identification strategy and on the estimates of the Local Average Treatment Effects of the

scholarship on students’ academic performance.

7 Conclusions

This paper has studied the role of monetary incentives in an educational context. In economic theoy,

monetary incentives are the standard tool to shape indiviual actions and effort and, nowadays,

18

many policy interventions are designed in order to use this principle to influence individual decision

making. Recently, more and more attention is dedicated on how financial incentives could serve

also to condition students behavior in their academic studies. The present paper, in particular, is

focused on a public insistution designed principally to provide fair access and equal opportunity

in higher education. As it emerges from the results of this research, the scholarships provided for

low-income students, could not only respond to the equity purpose cited above, but they could

also be used as active tools to boost students performances. In particular, I found that on average

e840 more will bring students to get from 9 to 18 credits more by the end of the year, with gains

also for their GPA, i.e around two points more (the 7% of the average mark). Moreover, I found

that the scholarships are more effective in this purpose for lower income students (first threshols

results), suggesting that policy maker who wants to magnify such effect should allocate more and/or

higher scholarship for this type of students. In line with the literature, I also find gender gaps. In

particular, female students drive the results on the GPA (probably because they acquire already

an above average amount of credits) while male students drive the finding on credits. The results

presented seem to be valid and robust to different consistency checks as I shown in the robustness

section.

With the present paper, I want to add new causal evidence on how financial incentives shape

individual actions, particularly in an educational context. I find that pure monetary incentives can

significantly increase academic performance in a context of lower income students. Moreover, the

aim of this research is also to provide recommendations for policy maker on how to properly design

instutions that want to effectively boost academic perfromances. Further research should explain

through which channel the financial incentives are working. In particular, is suggested to acquire

additional information in order to understand if students are studying better just because of the

threath of loosing the scholarship and/or because they have a positive shock on their income, with

consequences on their labor supply. Responding to such questions, will allow to better target the

scholarship and the related requirements.

19

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23

8 Appendix

Table 3: Descriptive Statistics

Variables Obs Mean Std. Dev. Min Max

Female 2430 0.6 0.48 0 1Age 2430 21.44 3.53 18 53

RegionCenter 2430 0.01 0.11 0 1

Center - North 2430 0.11 0.32 0 1Center - South 2430 0.04 0.21 0 1

Foreign 2430 0.06 0.24 0 1Islands 2430 0.09 0.29 0 1North 2430 0.37 0.48 0 1South 2430 0.28 0.45 0 1

High School Grade 2301 81.66 13.48 60 100Credits 2425 30.88 16.16 0 130

Average 1750 26 2.83 18 30

Scholarship typeIn sede 2430 0.4 0.49 0 1

Pendolari 2430 0.12 0.32 0 1Fuori Sede 2430 0.47 0.49 0 1

Note. Statistics for the freshman who enrolled at University of Bologna in the 2008/2009 academic year.

24

Table 4: Grant Effects on Students’ Achievement

Local Linear Polynom. 2nd Polynom. 3rd Polynom. 4th± 500 ± 850 ± 1200 ± 1400

Panel A: Credits

Higher Scholarships’ Effect 9.646** 10.141** 15.320*** 18.852***(3.951) (4.286) (4.722) (5.356)

Constant 25.602*** 24.944*** 21.076*** 18.280***(3.224) (3.329) (3.743) (4.292)

Obs 312 535 754 865

Panel B: Average Grade

Higher Scholarships’ Effect 1.867** 1.792* 2.063** 1.930*(0.846) (0.940) (1.026) (1.159)

Constant 25.157*** 24.935*** 24.764*** 24.919***(0.709) (0.774) (0.846) (0.956)

Obs 232 392 557 641

Panel C: Performance

Higher Scholarships’ Effect 0.106** 0.090* 0.113** 0.127**(0.044) (0.047) (0.052) (0.061)

Constant 0.670*** 0.674*** 0.652*** 0.645***(0.038) (0.038) (0.043) (0.051)

Obs 232 392 557 641

Panel D: P(Credits’Requirements)


Constant 0.631*** 0.603*** 0.519*** 0.415***(0.083) (0.094) (0.106) (0.121)

Obs 312 535 754 865

Note. OLS estimates of equation:

Zi = α+ F (Yi) + γDi + εi (4)

where Zi is the vector of outcomes variables (Credits, Average Grade, Performance, P(Credit Requirements));F (Yi) is a vector whose elements are two polynomials (one for each side of the threshold) in the absolutedifference between the ISEE declaration and the thresholds ; Di is a dummy taking value 1 for students on theleft side of the threshold. Robust standard errors in parentheses.

25

Table 5: Grant Effects on “Fuori Corso” proxy


Panel D: P(60 Credits)

Higher Scholarships’ Effect 0.057 0.054 0.086 0.100(0.062) (0.069) (0.078) (0.091)

Constant 0.045 0.044 0.033 0.035(0.043) (0.045) (0.052) (0.062)

R-squared 0.006 0.003 0.011 0.013Obs 312 535 754 865


Zi = α+ F (Yi) + γDi + εi (5)

where Zi is the outcomes variable P(Regular Credit Requirements). Credit Requirements is a dummy takingvalue 1 for students who have completed, at least, 60 credits in the academic year. Robust standard errors inparentheses.

26

Table 6: First Threshold Effects


Panel A: Credits

First Threshold Effect (950e) 11.863** 11.955* 18.113*** 23.483***(5.639) (6.127) (6.884) (8.117)

Constant 23.245*** 22.843*** 18.198*** 15.648**(4.715) (4.787) (5.459) (6.411)

Obs 162 273 369 421


First Threshold Effect (950e) 3.026*** 2.845** 2.637* 2.248(1.044) (1.255) (1.363) (1.533)

Constant 24.482*** 24.443*** 24.537*** 24.699***(0.826) (1.022) (1.117) (1.275)

Obs 124 204 280 320


First Threshold Effect (950e) 0.186*** 0.154** 0.175** 0.191**(0.062) (0.047) (0.077) (0.092)

Constant 0.608*** 0.634*** 0.605*** 0.606***(0.053) (0.057) (0.064) (0.076)

Obs 124 204 280 320


First Threshold Effect (950e) 0.203 0.324* 0.480*** 0.649***(0.143) (0.165) (0.184) (0.209)

Constant 0.652*** 0.521*** 0.521*** 0.255(0.119) (0.135) (0.148) (0.160)

Obs 162 273 369 421


Zi = α+ F (Yi) + γDi + εi (6)

where Zi is the vector of outcomes variables (Credits, Average Grade, Performance, P(Credit Requirements));F (Yi) is a vector whose elements are two polynomials (one for each side of the threshold) in the absolutedifference between the ISEE declaration and the thresholds ; Di is a dummy taking value 1 for students on theleft side of the threshold. Robust standard errors in parentheses.

27

Table 7: Second Threshold Effect


Panel A: Credits

Second Threshold Effect (730e) 7.093 8.989 13.296** 15.193**(5.504) (6.130) (6.612) (7.249)

Constant 28.177*** 26.520*** 23.642*** 20.691***(4.305) (4.777) (5.260) (5.942)

Obs 150 262 385 444

Panel B: Grades’Average

Second Threshold Effect (730e) 0.448 0.530 1.169 1.323(1.370) (1.424) (1.520) (1.685)

Constant 26.020*** 25.471*** 25.018*** 25.193***(1.203) (1.221) (1.285) (1.390)

Obs 108 188 277 321


Second Threshold Effect (730e) 0.000 0.011 0.036 0.047(0.056) (0.059) (0.063) (0.069)

Constant 0.753*** 0.724*** 0.711*** 0.695***(0.044) (0.047) (0.050) (0.054)

Obs 108 188 277 321


Second Threshold Effect (730e) 0.248* 0.241 0.307* 0.323(0.140) (0.157) (0.179) (0.209)

Constant 0.610*** 0.650*** 0.602*** 0.561***(0.117) (0.129) (0.150) (0.177)

Obs 150 262 385 444


Zi = α+ F (Yi) + γDi + εi (7)

where Zi is the vector of outcomes variables (Credits, Average Grade, Performance, P(Credits’ Requirements));F (Yi) is a vector whose elements are two polynomials (one for each side of the threshold) in the absolutedifference between the ISEE declaration and the thresholds ; Di is a dummy taking value 1 for students on theleft side of the threshold. Robust standard errors in parentheses.

28

Table 8: Female Estimates


(a) FemalesPanel A: Credits


Constant 30.845*** 33.134*** 29.954*** 30.872***(3.91) (4.00) -4.43 -4.99

Obs 206 340 472 537


Higher Scholarships’ Effect 2.320** 2.156** 2.540** 1.73(0.94) (1.04) (1.14) (1.2)

Constant 25.189*** 25.100*** 24.943*** 25.777***(0.82) (0.89) (0.96) (0.97)

Obs 161 266 369 418


Higher Scholarships’ Effect 0.107** 0.076 0.102* 0.086(0.05) (0.05) (0.06) (0.07)

Constant 0.666*** 0.689*** 0.666*** 0.683***(0.04) (0.05) (0.05) (0.06)

Obs 161 266 369 418



Constant 0.746*** 0.750*** 0.639*** 0.607***(0.1) (0.11) (0.13) (0.16)

Obs 206 340 472 537

Note: The reported results on outcome variables are only for female scholarship recipients enrolled at Universityof Bologna. OLS estimates of equation:

Zi = α+ F (Yi) + γDi + εi (8)


29

Table 9: Male Estimates


(a) MalesPanel A: Credits

Higher Scholarship Effect 17.630*** 19.713*** 28.452*** 35.753***(6.36) (7.01) (7.43) (8.16)

Constant 16.442*** 14.173*** 9.702** 3.427(4.46) (4.52) (4.86) (5.07)

Obs 106 195 282 328


Higher Scholarship Effect 0.896 1.246 1.435 2.899(1.8) (1.91) (2.12) (2.71)

Constant 25.147*** 24.388*** 24.267*** 22.632***(1.47) (1.51) (1.72) (2.35)

Obs 71 126 188 223


Higher Scholarship Effect 0.09 0.127 0.159 0.244*(0.08) (0.09) (0.1) (0.13)

Constant 0.691*** 0.632*** 0.615*** 0.532***(0.07) (0.07) (0.08) (0.1)

Obs 71 126 188 223



Constant 0.451*** 0.405*** 0.360** 0.197(0.15) (0.14) (0.16) (0.16)

Obs 106 195 282 328

Note: The reported results on outcome variables are for male scholarship recipients enrolled at University ofBologna. OLS estimates of equation:

Zi = α+ F (Yi) + γDi + εi (9)


30

Table 10: Regressions on Pre-treatment variables


Panel A: SexHigher Scholarship Effect 0.032 0.006 0.001 0.163

(0.11) (0.13) (0.14) (0.17)Constant 0.545*** 0.568*** 0.557*** 0.469***

(0.08) (0.10) (0.11) (0.13)Obs 321 552 780 899

Panel B: AgeHigher Scholarship Effect 0.999 0.911 0.449 -0.412

(0.71) (0.81) (0.97) (1.15)Constant 21.441*** 21.497*** 21.785*** 22.494***

(0.47) (0.54) (0.65) (0.80)Obs 321 552 780 899

Panel C: High-School GradeHigher Scholarship Effect 4.699 6.291 6.313 8.413

(3.44) (3.88) (4.41) (5.17)Constant 80.935*** 79.648*** 78.287*** 76.720***

(2.57) (2.87) (3.29) (3.89)Obs 296 510 725 836

Panel D: ISPEHigher Scholarship Effect 1536.229 2029.465 1445.873 3657.112

(2607.57) (3033.52) (3415.19) (4043.05)Constant 10063.320*** 10864.235*** 11198.737*** 10595.645***

(1855.30) (2142.27) (2480.20) (2968.13)

Obs 321 552 780 899

Panel E: P(Fuori Sede)Higher Scholarship Effect 0.049 0.072 0.096 0.024

(0.11) (0.13) (0.15) (0.17)Constant 0.411*** 0.367*** 0.349*** 0.408***

(0.08) (0.10) (0.11) (0.13)

Obs 321 552 780 899


Vi = α+ F (Yi) + γDi + εi (10)

where Vi is the vector of pre-treatment variables (Sex, Age, High-School Grade, ISPE, P(Fuori Sede)); F (Yi)is a vector whose elements are two polynomials (one for each side of the threshold) in the absolute differencebetween the ISEE declaration and the thresholds ; Di is a dummy taking value 1 for students on the left sideof the threshold. Robust standard errors in parentheses.

31

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als.

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ng

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Age

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est

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ents

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the

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the

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Gra

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ing

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hes

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ede”

sch

ola

rsh

ip.

33

Figure 3: Test on Manipulation

Note.Scatter points of density of observations around the aggregate threshold. The lateral lines are the 95%confidence interval.

34

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